The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities.

数字能源互联网（IoE）的数字领域即将在集成边缘人工智能（AI）的帮助下发生革命性转变。本综述详细阐述了边缘 AI 对重塑 IoE 生态系统的承诺和潜力。通过精心策划的研究方法，文章深入探讨了专门为 IoE 量身定制的众多边缘 AI 技术。从降低延迟和实时分析到信息安全、可扩展性和成本效益等关键方面的众多好处，凸显了边缘 AI 在现代 IoE 框架中的不可或缺性。随着叙述的深入，读者将了解到实际应用和技术，重点关注设备上的计算、安全的私有推理方法以及边缘 AI 训练的前沿范式。随后进行了一项关键分析，探讨了目前面临的挑战，包括安全问题、计算难题和标准化问题。然而，随着技术的不断拓展，综述以展望未来的观点告终，设想了 5G 网络、联邦边缘 AI、深度强化学习等未来的共生关系，描绘了未来所展示的生动画面。对于在 IoE 和 AI 领域投入的任何人来说，本综述既提供了基础知识，又以一种有远见的眼光，将现实与未来的可能性联系起来。

边缘人工智能在能源互联网中的挑战与前景

Edge AI for Internet of Energy: Challenges and Perspectives

Federated learning is a distributed, on-device computation framework that
enables training global models without exporting sensitive user data to
servers. In this work, we describe methods to extend the federation framework
to evaluate strategies for personalization of global models. We present tools
to analyze the effects of personalization and evaluate conditions under which
personalization yields desirable models. We report on our experiments
personalizing a language model for a virtual keyboard for smartphones with a
population of tens of millions of users. We show that a significant fraction of
users benefit from personalization.

该研究介绍了扩展联邦学习运算框架以评估全球模型个性化策略的方法，并提出了分析个性化效果以评估个性化是否产生理想模型的工具。作者还报告了在数千万用户的智能手机虚拟键盘中个性化语言模型的实验结果，并展示了相当一部分用户从个性化中受益。

设备上个性化联邦评估

Federated Evaluation of On-device Personalization

TensorFlow.js is a library for building and executing machine learning
algorithms in JavaScript. TensorFlow.js models run in a web browser and in the
Node.js environment. The library is part of the TensorFlow ecosystem, providing
a set of APIs that are compatible with those in Python, allowing models to be
ported between the Python and JavaScript ecosystems. TensorFlow.js has
empowered a new set of developers from the extensive JavaScript community to
build and deploy machine learning models and enabled new classes of on-device
computation. This paper describes the design, API, and implementation of
TensorFlow.js, and highlights some of the impactful use cases.

本文介绍了 TensorFlow.js 的设计、API 和实现，它是一个用于在 JavaScript 中构建和执行机器学习算法的库，支持在 Web 浏览器和 Node.js 环境下运行模型，支持 Python API，使得模型可以在 Python 和 JavaScript 生态系统之间移植，从而使得来自广大 JavaScript 社区的新型开发人员可以构建和部署机器学习模型，并启用了一些新型的边缘设备计算。